{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T08:31:44.936725Z",
     "start_time": "2024-05-02T08:31:40.034353600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=9, micro=7, releaselevel='final', serial=0)\n",
      "matplotlib 3.8.4\n",
      "numpy 1.26.4\n",
      "pandas 2.2.2\n",
      "sklearn 1.4.2\n",
      "torch 2.2.2+cpu\n",
      "cpu\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "    \n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42\n",
    "torch.manual_seed(seed)\n",
    "torch.cuda.manual_seed_all(seed)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T08:32:04.220762800Z",
     "start_time": "2024-05-02T08:31:48.083348400Z"
    }
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "#用karas有的数据集imdb，电影分类,分电影是积极的，还是消极的\n",
    "imdb = keras.datasets.imdb\n",
    "#载入数据使用下面两个参数\n",
    "vocab_size = 10000  #词典大小，仅保留训练数据中前10000个最经常出现的单词，低频单词被舍弃\n",
    "index_from = 3  #0,1,2,3空出来做别的事\n",
    "#前一万个词出现词频最高的会保留下来进行处理，后面的作为特殊字符处理，\n",
    "# 小于3的id都是特殊字符，下面代码有写\n",
    "# 需要注意的一点是取出来的词表还是从1开始的，需要做处理\n",
    "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(\n",
    "    num_words = vocab_size, index_from = index_from)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T08:32:14.425129500Z",
     "start_time": "2024-05-02T08:32:14.402142900Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train 25000 (25000,)\n",
      "test 25000 (25000,)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"train\", len(train_data), train_labels.shape)  #25000个样本，每个样本是一段话，每个单词用一个数字表示\n",
    "print(\"test\", len(test_data), test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T08:32:17.066012200Z",
     "start_time": "2024-05-02T08:32:16.837139900Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88584\n",
      "<class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "#载入词表，看下词表长度，词表就像英语字典\n",
    "word_index = imdb.get_word_index()\n",
    "print(len(word_index))\n",
    "print(type(word_index))\n",
    "#词表虽然有8万多，但是我们只载入了最高频的1万词！！！！"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 构造 word2idx 和 idx2word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T08:32:20.506027300Z",
     "start_time": "2024-05-02T08:32:20.420076700Z"
    }
   },
   "outputs": [],
   "source": [
    "word2idx = {word: idx + 3 for word, idx in word_index.items()}\n",
    "word2idx.update({\n",
    "    \"[PAD]\": 0,     # 填充 token\n",
    "    \"[BOS]\": 1,     # begin of sentence\n",
    "    \"[UNK]\": 2,     # 未知 token\n",
    "    \"[EOS]\": 3,     # end of sentence\n",
    "})\n",
    "\n",
    "idx2word = {idx: word for word, idx in word2idx.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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   "source": [
    "# 选择 max_length\n",
    "length_collect = {}\n",
    "for text in train_data:\n",
    "    length = len(text)\n",
    "    length_collect[length] = length_collect.get(length, 0) + 1\n",
    "    \n",
    "MAX_LENGTH = 500\n",
    "plt.bar(length_collect.keys(), length_collect.values())\n",
    "plt.axvline(MAX_LENGTH, label=\"max length\", c=\"gray\", ls=\":\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T08:32:28.841714100Z",
     "start_time": "2024-05-02T08:32:28.818725300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw text\n",
      "['hello', 'world']\n",
      "['tokenize', 'text', 'datas', 'with', 'batch']\n",
      "['this', 'is', 'a', 'test']\n",
      "indices\n",
      "tensor([   0,    0,    0,    1, 4825,  182,    3])\n",
      "tensor([    1,     2,  3004,     2,    19, 19233,     3])\n",
      "tensor([   0,    1,   14,    9,    6, 2181,    3])\n",
      "decode text\n",
      "[PAD] [PAD] [PAD] [BOS] hello world [EOS]\n",
      "[BOS] [UNK] text [UNK] with batch [EOS]\n",
      "[PAD] [BOS] this is a test [EOS]\n"
     ]
    }
   ],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=500, pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "    \n",
    "    def encode(self, text_list, padding_first=False):\n",
    "        \"\"\"如果padding_first == True，则padding加载前面，否则加载后面\"\"\"\n",
    "        max_length = min(self.max_length, 2 + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            indices = [self.bos_idx] + [self.word2idx.get(word, self.unk_idx) for word in text[:max_length-2]] + [self.eos_idx]\n",
    "            if padding_first:\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        return torch.tensor(indices_list)\n",
    "    \n",
    "    \n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":\n",
    "                    break\n",
    "                text.append(word)\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "    \n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "indices = tokenizer.encode(raw_text, padding_first=True)\n",
    "decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "print(\"raw text\")\n",
    "for raw in raw_text:\n",
    "    print(raw)\n",
    "print(\"indices\")\n",
    "for index in indices:\n",
    "    print(index)\n",
    "print(\"decode text\")\n",
    "for decode in decode_text:\n",
    "    print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"[BOS] this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert [UNK] is an amazing actor and now the same being director [UNK] father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for [UNK] and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also [UNK] to the two little boy's that played the [UNK] of norman and paul they were just brilliant children are often left out of the [UNK] list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\",\n",
       " \"[BOS] big hair big boobs bad music and a giant safety pin these are the words to best describe this terrible movie i love cheesy horror movies and i've seen hundreds but this had got to be on of the worst ever made the plot is paper thin and ridiculous the acting is an abomination the script is completely laughable the best is the end showdown with the cop and how he worked out who the killer is it's just so damn terribly written the clothes are sickening and funny in equal [UNK] the hair is big lots of boobs [UNK] men wear those cut [UNK] shirts that show off their [UNK] sickening that men actually wore them and the music is just [UNK] trash that plays over and over again in almost every scene there is trashy music boobs and [UNK] taking away bodies and the gym still doesn't close for [UNK] all joking aside this is a truly bad film whose only charm is to look back on the disaster that was the 80's and have a good old laugh at how bad everything was back then\"]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看训练集的数据\n",
    "\n",
    "tokenizer.decode(train_data[:2], remove_bos=False, remove_eos=False, remove_pad=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据集与 DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T08:32:41.820591900Z",
     "start_time": "2024-05-02T08:32:36.227566100Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "class IMDBDataset(Dataset):\n",
    "    def __init__(self, data, labels, remain_length=True):\n",
    "        if remain_length:\n",
    "            self.data = tokenizer.decode(data, remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "        else:\n",
    "            # 缩减一下数据\n",
    "            self.data = tokenizer.decode(data)\n",
    "        self.labels = labels\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        text = self.data[index]\n",
    "        label = self.labels[index]\n",
    "        return text, label\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "    \n",
    "    \n",
    "def collate_fct(batch):\n",
    "    text_list = [item[0].split() for item in batch]\n",
    "    label_list = [item[1] for item in batch]\n",
    "    # 这里使用 padding first\n",
    "    text_list = tokenizer.encode(text_list, padding_first=True).to(dtype=torch.int)\n",
    "    return text_list, torch.tensor(label_list).reshape(-1, 1).to(dtype=torch.float)\n",
    "\n",
    "\n",
    "# 用RNN，缩短序列长度\n",
    "train_ds = IMDBDataset(train_data, train_labels, remain_length=False)\n",
    "test_ds = IMDBDataset(test_data, test_labels, remain_length=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T08:33:12.220977300Z",
     "start_time": "2024-05-02T08:33:12.195716900Z"
    }
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True, collate_fn=collate_fct)\n",
    "test_dl = DataLoader(test_ds, batch_size=batch_size, shuffle=False, collate_fn=collate_fct)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T08:33:48.336669800Z",
     "start_time": "2024-05-02T08:33:48.283700100Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================== 一层单向 LSTM ===================================\n",
      "            embeding.weight             paramerters num: 160000\n",
      "           lstm.weight_ih_l0            paramerters num: 4096\n",
      "           lstm.weight_hh_l0            paramerters num: 16384\n",
      "            lstm.bias_ih_l0             paramerters num: 256\n",
      "            lstm.bias_hh_l0             paramerters num: 256\n",
      "              layer.weight              paramerters num: 4096\n",
      "               layer.bias               paramerters num: 64\n",
      "               fc.weight                paramerters num: 64\n",
      "                fc.bias                 paramerters num: 1\n",
      "================================== 一层双向 LSTM ===================================\n",
      "            embeding.weight             paramerters num: 160000\n",
      "           lstm.weight_ih_l0            paramerters num: 4096\n",
      "           lstm.weight_hh_l0            paramerters num: 16384\n",
      "            lstm.bias_ih_l0             paramerters num: 256\n",
      "            lstm.bias_hh_l0             paramerters num: 256\n",
      "       lstm.weight_ih_l0_reverse        paramerters num: 4096\n",
      "       lstm.weight_hh_l0_reverse        paramerters num: 16384\n",
      "        lstm.bias_ih_l0_reverse         paramerters num: 256\n",
      "        lstm.bias_hh_l0_reverse         paramerters num: 256\n",
      "              layer.weight              paramerters num: 8192\n",
      "               layer.bias               paramerters num: 64\n",
      "               fc.weight                paramerters num: 64\n",
      "                fc.bias                 paramerters num: 1\n",
      "================================== 两层单向 LSTM ===================================\n",
      "            embeding.weight             paramerters num: 160000\n",
      "           lstm.weight_ih_l0            paramerters num: 4096\n",
      "           lstm.weight_hh_l0            paramerters num: 16384\n",
      "            lstm.bias_ih_l0             paramerters num: 256\n",
      "            lstm.bias_hh_l0             paramerters num: 256\n",
      "           lstm.weight_ih_l1            paramerters num: 16384\n",
      "           lstm.weight_hh_l1            paramerters num: 16384\n",
      "            lstm.bias_ih_l1             paramerters num: 256\n",
      "            lstm.bias_hh_l1             paramerters num: 256\n",
      "              layer.weight              paramerters num: 4096\n",
      "               layer.bias               paramerters num: 64\n",
      "               fc.weight                paramerters num: 64\n",
      "                fc.bias                 paramerters num: 1\n"
     ]
    }
   ],
   "source": [
    "class LSTM(nn.Module):\n",
    "    def __init__(self, embedding_dim=16, hidden_dim=64, vocab_size=vocab_size, num_layers=1, bidirectional=False):\n",
    "        super(LSTM, self).__init__()\n",
    "        self.embeding = nn.Embedding(vocab_size, embedding_dim)\n",
    "        self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers=num_layers, batch_first=True, bidirectional=bidirectional)\n",
    "        self.layer = nn.Linear(hidden_dim * (2 if bidirectional else 1), hidden_dim)\n",
    "        self.fc = nn.Linear(hidden_dim, 1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # [bs, seq length]\n",
    "        x = self.embeding(x)\n",
    "        # [bs, seq length, embedding_dim] -> shape [bs, embedding_dim, seq length]\n",
    "        seq_output, (hidden, cell) = self.lstm(x)\n",
    "        # [bs, seq length, hidden_dim], [*, bs, hidden_dim]\n",
    "        x = seq_output[:, -1, :]\n",
    "        # 取最后一个时间步的输出 (这也是为什么要设置padding_first=True的原因)\n",
    "        x = self.layer(x)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "    \n",
    "sample_inputs = torch.randint(0, vocab_size, (2, 128))\n",
    "    \n",
    "print(\"{:=^80}\".format(\" 一层单向 LSTM \"))       \n",
    "for key, value in LSTM().named_parameters():\n",
    "    print(f\"{key:^40}paramerters num: {np.prod(value.shape)}\")\n",
    "\n",
    "    \n",
    "print(\"{:=^80}\".format(\" 一层双向 LSTM \"))       \n",
    "for key, value in LSTM(bidirectional=True).named_parameters():\n",
    "    print(f\"{key:^40}paramerters num: {np.prod(value.shape)}\")\n",
    "\n",
    "    \n",
    "print(\"{:=^80}\".format(\" 两层单向 LSTM \"))       \n",
    "for key, value in LSTM(num_layers=2).named_parameters():\n",
    "    print(f\"{key:^40}paramerters num: {np.prod(value.shape)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "4096"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4 * 16 * 64 #lstm.weight_ih_l0"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-02T08:36:09.545313600Z",
     "start_time": "2024-05-02T08:36:09.506335700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "16384"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4* 64*64 #lstm.weight_hh_l0"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-02T08:36:14.836046500Z",
     "start_time": "2024-05-02T08:36:14.799050900Z"
    }
   }
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "@torch.no_grad()\n",
    "def evaluating(model, dataloader, loss_fct):\n",
    "    loss_list = []\n",
    "    pred_list = []\n",
    "    label_list = []\n",
    "    for datas, labels in dataloader:\n",
    "        datas = datas.to(device)\n",
    "        labels = labels.to(device)\n",
    "        # 前向计算\n",
    "        logits = model(datas)\n",
    "        loss = loss_fct(logits, labels)         # 验证集损失\n",
    "        loss_list.append(loss.item())\n",
    "        # 二分类\n",
    "        preds = logits > 0\n",
    "        pred_list.extend(preds.cpu().numpy().tolist())\n",
    "        label_list.extend(labels.cpu().numpy().tolist())\n",
    "        \n",
    "    acc = accuracy_score(label_list, pred_list)\n",
    "    return np.mean(loss_list), acc\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TensorBoard 可视化\n",
    "\n",
    "\n",
    "训练过程中可以使用如下命令启动tensorboard服务。\n",
    "\n",
    "```shell\n",
    "tensorboard \\\n",
    "    --logdir=runs \\     # log 存放路径\n",
    "    --host 0.0.0.0 \\    # ip\n",
    "    --port 8848         # 端口\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "\n",
    "class TensorBoardCallback:\n",
    "    def __init__(self, log_dir, flush_secs=10):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            log_dir (str): dir to write log.\n",
    "            flush_secs (int, optional): write to dsk each flush_secs seconds. Defaults to 10.\n",
    "        \"\"\"\n",
    "        self.writer = SummaryWriter(log_dir=log_dir, flush_secs=flush_secs)\n",
    "\n",
    "    def draw_model(self, model, input_shape):\n",
    "        self.writer.add_graph(model, input_to_model=torch.randn(input_shape))\n",
    "        \n",
    "    def add_loss_scalars(self, step, loss, val_loss):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/loss\", \n",
    "            tag_scalar_dict={\"loss\": loss, \"val_loss\": val_loss},\n",
    "            global_step=step,\n",
    "            )\n",
    "        \n",
    "    def add_acc_scalars(self, step, acc, val_acc):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/accuracy\",\n",
    "            tag_scalar_dict={\"accuracy\": acc, \"val_accuracy\": val_acc},\n",
    "            global_step=step,\n",
    "        )\n",
    "        \n",
    "    def add_lr_scalars(self, step, learning_rate):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/learning_rate\",\n",
    "            tag_scalar_dict={\"learning_rate\": learning_rate},\n",
    "            global_step=step,\n",
    "            \n",
    "        )\n",
    "    \n",
    "    def __call__(self, step, **kwargs):\n",
    "        # add loss\n",
    "        loss = kwargs.pop(\"loss\", None)\n",
    "        val_loss = kwargs.pop(\"val_loss\", None)\n",
    "        if loss is not None and val_loss is not None:\n",
    "            self.add_loss_scalars(step, loss, val_loss)\n",
    "        # add acc\n",
    "        acc = kwargs.pop(\"acc\", None)\n",
    "        val_acc = kwargs.pop(\"val_acc\", None)\n",
    "        if acc is not None and val_acc is not None:\n",
    "            self.add_acc_scalars(step, acc, val_acc)\n",
    "        # add lr\n",
    "        learning_rate = kwargs.pop(\"lr\", None)\n",
    "        if learning_rate is not None:\n",
    "            self.add_lr_scalars(step, learning_rate)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Best\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        \"\"\"\n",
    "        Save checkpoints each save_epoch epoch. \n",
    "        We save checkpoint by epoch in this implementation.\n",
    "        Usually, training scripts with pytorch evaluating model and save checkpoint by step.\n",
    "\n",
    "        Args:\n",
    "            save_dir (str): dir to save checkpoint\n",
    "            save_epoch (int, optional): the frequency to save checkpoint. Defaults to 1.\n",
    "            save_best_only (bool, optional): If True, only save the best model or save each model at every epoch.\n",
    "        \"\"\"\n",
    "        self.save_dir = save_dir\n",
    "        self.save_step = save_step\n",
    "        self.save_best_only = save_best_only\n",
    "        self.best_metrics = -1\n",
    "        \n",
    "        # mkdir\n",
    "        if not os.path.exists(self.save_dir):\n",
    "            os.mkdir(self.save_dir)\n",
    "        \n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return\n",
    "        \n",
    "        if self.save_best_only:\n",
    "            assert metric is not None\n",
    "            if metric >= self.best_metrics:\n",
    "                # save checkpoints\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"best.ckpt\"))\n",
    "                # update best metrics\n",
    "                self.best_metrics = metric\n",
    "        else:\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Early Stop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            patience (int, optional): Number of epochs with no improvement after which training will be stopped.. Defaults to 5.\n",
    "            min_delta (float, optional): Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute \n",
    "                change of less than min_delta, will count as no improvement. Defaults to 0.01.\n",
    "        \"\"\"\n",
    "        self.patience = patience\n",
    "        self.min_delta = min_delta\n",
    "        self.best_metric = -1\n",
    "        self.counter = 0\n",
    "        \n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:\n",
    "            # update best metric\n",
    "            self.best_metric = metric\n",
    "            # reset counter \n",
    "            self.counter = 0\n",
    "        else: \n",
    "            self.counter += 1\n",
    "            \n",
    "    @property\n",
    "    def early_stop(self):\n",
    "        return self.counter >= self.patience\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "358756a8847d4896ad3b5b0c6766c07a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3920 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 训练\n",
    "def training(\n",
    "    model, \n",
    "    train_loader, \n",
    "    val_loader, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    tensorboard_callback=None,\n",
    "    save_ckpt_callback=None,\n",
    "    early_stop_callback=None,\n",
    "    eval_step=500,\n",
    "    ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "    \n",
    "    global_step = 0\n",
    "    model.train()\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # training\n",
    "            for datas, labels in train_loader:\n",
    "                datas = datas.to(device)\n",
    "                labels = labels.to(device)\n",
    "                # 梯度清空\n",
    "                optimizer.zero_grad()\n",
    "                # 模型前向计算\n",
    "                logits = model(datas)\n",
    "                # 计算损失\n",
    "                loss = loss_fct(logits, labels)\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                preds = logits > 0\n",
    "            \n",
    "                acc = accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())    \n",
    "                loss = loss.cpu().item()\n",
    "                # record\n",
    "                \n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss, \"acc\": acc, \"step\": global_step\n",
    "                })\n",
    "                \n",
    "                # evaluating\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()\n",
    "                    val_loss, val_acc = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss, \"acc\": val_acc, \"step\": global_step\n",
    "                    })\n",
    "                    model.train()\n",
    "                    \n",
    "                    # 1. 使用 tensorboard 可视化\n",
    "                    if tensorboard_callback is not None:\n",
    "                        tensorboard_callback(\n",
    "                            global_step, \n",
    "                            loss=loss, val_loss=val_loss,\n",
    "                            acc=acc, val_acc=val_acc,\n",
    "                            lr=optimizer.param_groups[0][\"lr\"],\n",
    "                            )\n",
    "                \n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), metric=val_acc)\n",
    "\n",
    "                    # 3. 早停 Early Stop\n",
    "                    if early_stop_callback is not None:\n",
    "                        early_stop_callback(val_acc)\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict\n",
    "                    \n",
    "                # udate step\n",
    "                global_step += 1\n",
    "                pbar.update(1)\n",
    "                pbar.set_postfix({\"epoch\": epoch_id})\n",
    "        \n",
    "    return record_dict\n",
    "        \n",
    "\n",
    "epoch = 20\n",
    "\n",
    "model = LSTM()\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失 (但是二分类)\n",
    "loss_fct = F.binary_cross_entropy_with_logits\n",
    "# 2. 定义优化器 采用 adam\n",
    "# Optimizers specified in the torch.optim package\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 1. tensorboard 可视化\n",
    "if not os.path.exists(\"runs\"):\n",
    "    os.mkdir(\"runs\")\n",
    "tensorboard_callback = TensorBoardCallback(\"runs/imdb-lstm\")\n",
    "# tensorboard_callback.draw_model(model, [1, MAX_LENGTH])\n",
    "# 2. save best\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(\"checkpoints/imdb-lstm\", save_step=len(train_dl), save_best_only=True)\n",
    "# 3. early stop\n",
    "early_stop_callback = EarlyStopCallback(patience=10)\n",
    "\n",
    "model = model.to(device)\n",
    "record = training(\n",
    "    model, \n",
    "    train_dl, \n",
    "    test_dl, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    tensorboard_callback=tensorboard_callback,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=len(train_dl)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "image/svg+xml": [
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       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"601.806633pt\" height=\"321.95625pt\" viewBox=\"0 0 601.806633 321.95625\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
       " <metadata>\n",
       "  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
       "   <cc:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
       "    <dc:date>2023-04-24T22:22:36.139966</dc:date>\n",
       "    <dc:format>image/svg+xml</dc:format>\n",
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   "source": [
    "#画线要注意的是损失是不一定在零到1之间的\n",
    "def plot_learning_curves(record_dict, sample_step=500):\n",
    "    # build DataFrame\n",
    "    train_df = pd.DataFrame(record_dict[\"train\"]).set_index(\"step\").iloc[::sample_step]\n",
    "    val_df = pd.DataFrame(record_dict[\"val\"]).set_index(\"step\")\n",
    "\n",
    "    # plot\n",
    "    fig_num = len(train_df.columns)\n",
    "    fig, axs = plt.subplots(1, fig_num, figsize=(5 * fig_num, 5))\n",
    "    for idx, item in enumerate(train_df.columns):    \n",
    "        axs[idx].plot(train_df.index, train_df[item], label=f\"train_{item}\")\n",
    "        axs[idx].plot(val_df.index, val_df[item], label=f\"val_{item}\")\n",
    "        axs[idx].grid()\n",
    "        axs[idx].legend()\n",
    "        # axs[idx].set_xticks(range(0, train_df.index[-1], 5000))\n",
    "        # axs[idx].set_xticklabels(map(lambda x: f\"{int(x/1000)}k\", range(0, train_df.index[-1], 5000)))\n",
    "        axs[idx].set_xlabel(\"step\")\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "plot_learning_curves(record, sample_step=10)  #横坐标是 steps"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:     0.3489\n",
      "accuracy: 0.8803\n"
     ]
    }
   ],
   "source": [
    "# dataload for evaluating\n",
    "\n",
    "# load checkpoints\n",
    "model.load_state_dict(torch.load(\"checkpoints/imdb-lstm/best.ckpt\", map_location=\"cpu\"))\n",
    "\n",
    "model.eval()\n",
    "loss, acc = evaluating(model, test_dl, loss_fct)\n",
    "print(f\"loss:     {loss:.4f}\\naccuracy: {acc:.4f}\")"
   ]
  }
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